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      Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1–100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging. The combination of anatomical structure and time delays creates a space–time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

          Author Summary

          There has been a great deal of interest generated by the observation of resting-state or “default-mode” networks in the human brain. These networks seem to be most engaged when persons are not involved in overt goal-directed behavior. These networks are also thought to underlie certain aspects of conscious introspection and to be specific to humans. Our paper provides a new explanation for rest state fluctuations by suggesting that they reflect a deeper biological principle of organization and are a consequence of the space–time structure of primate anatomical connectivity. In a computational study using a biologically realistic primate cortical connectivity matrix, we show that the rest state networks emerge only if the time delays of signal transmission between brain areas are considered. The combination of anatomical structure and time delays creates a space–time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire. The latter repertoire spans temporal scales of multiple orders of magnitude including scales observed in electric potentials and magnetic fields on the scalp, as well as in blood flow signals. Our results provide a testable explanation of the real-world phenomenon of rest state fluctuations in the primate brain.

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          Most cited references26

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          Impulses and Physiological States in Theoretical Models of Nerve Membrane

          Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting "BVP model" has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a "physiological state diagram," with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable.
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            Searching for a baseline: functional imaging and the resting human brain.

            Functional brain imaging in humans has revealed task-specific increases in brain activity that are associated with various mental activities. In the same studies, mysterious, task-independent decreases have also frequently been encountered, especially when the tasks of interest have been compared with a passive state, such as simple fixation or eyes closed. These decreases have raised the possibility that there might be a baseline or resting state of brain function involving a specific set of mental operations. We explore this possibility, including the manner in which we might define a baseline and the implications of such a baseline for our understanding of brain function.
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              Rhythms of the Brain

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                October 2008
                October 2008
                10 October 2008
                : 4
                : 10
                : e1000196
                Affiliations
                [1 ]Theoretical Neuroscience Group, Institut des Sciences du Mouvement, Marseille, France
                [2 ]UMR6233, CNRS, Marseille, France
                [3 ]Center for Complex Systems and Brain Sciences, Physics Department, Florida Atlantic University, Boca Raton, Florida, United States of America
                [4 ]Rotman Research Institute of Baycrest Center, Toronto, Ontario, Canada
                [5 ]Department of Cognitive Neuroscience, University Medical Centre St. Radboud, Nijmegen, The Netherlands
                [6 ]Vogt Brain Research Institute and Anatomy II, Heinrich Heine University, Düsseldorf, Germany
                University College London, United Kingdom
                Author notes

                Analyzed the data: AG ARM RK VKJ. Contributed reagents/materials/analysis tools: AG YAR ARM RK VKJ. Wrote the paper: AG YAR ARM RK VKJ.

                Article
                08-PLCB-RA-0321R3
                10.1371/journal.pcbi.1000196
                2551736
                18846206
                1ec73f21-75fd-4fa9-b76e-4efdcfadf045
                Ghosh et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 29 April 2008
                : 2 September 2008
                Page count
                Pages: 12
                Categories
                Research Article
                Neuroscience/Theoretical Neuroscience

                Quantitative & Systems biology
                Quantitative & Systems biology

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